Evidence map›Paper›PMID 42679095›Full record

ArticleJMIR formative research2026

Representing Chronic Pain Experiences Through Manual and AI-Driven Vignettes From Video Blog Data With Member Checking: Participatory Design Study.

Shehnaz Fatima Lakha, Peter Pennefather, Alamgir Khandwala, Deborah I Fels

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shehnaz Fatima LakhaInclusive Media and Design Centre, Ted Rogers School of Managment, Toronto Metropolitan University, 55 Dundas Street W, Toronto, ON, M5G 2C3, Canada, 1 416-979-5000 ext 7110.ORCID 0000-0002-3325-7553
Peter PennefatherLeslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-2953-8977
Alamgir KhandwalaSchool of Administrative Studies, York University, Toronto, ON, Canada.ORCID 0009-0009-7110-8397
Deborah I FelsInclusive Media and Design Centre, Ted Rogers School of Managment, Toronto Metropolitan University, 55 Dundas Street W, Toronto, ON, M5G 2C3, Canada, 1 416-979-5000 ext 7110.ORCID 0000-0001-7295-3386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health care professional-generated vignettes are commonly used to illustrate and analyze patient experiences, shaped through clinical reflection to support provider understanding, training, and practice improvement. However, the subjective interpretation of narratives and the time-consuming manual process limit this approach. As an alternative, patients could record video blogs (vlogs) of their experiences, which can then be transformed into vignettes using GenAI and carefully designed prompts. Objective: This study aims to compare the feasibility and utility of 2 different vignette creation methods, manual and ChatGPT-3.0, from user-generated vlogs about living with chronic pain. Methods: Short commentary videos were recorded by a person living with chronic pain of unknown origin. These were transcribed and used to create vignettes. From 64 videos, three 1-minute clips were selected, each representing a key life stream: university student, international traveler, and patient with chronic pain, based on prior thematic analysis. In total, 3 vignettes were created for each creation method (manual and AI-generated). Each vignette incorporated a profile description, 3 selected user-generated video clips representing the participant's 3 life streams, researcher-generated analytic commentary for each clip (produced by a researcher with 15 years of experience in manual vignette creation for clinical use), and a brief summary of each video. A comparison between manually constructed and AI-generated vignettes evaluated coherence, contextual accuracy, narrative depth, and utility in health communication and user experience research. Additionally, the person responsible for generating the raw videos (Participant X) provided commentary on this process as a cocreator of a new method, offering critical insights into the authenticity, emotional resonance, and perceived usefulness of each vignette format. Results: This study demonstrates that manual and ChatGPT vignette creation methods are feasible approaches for analyzing and representing user-generated vlogs about living with chronic pain. Each method offers distinct utility: manual vignettes provide rich, nuanced narratives capturing emotional and social complexities, while ChatGPT-generated vignettes offer efficient, concise summaries with some loss of detail. Participant X's reflections reinforced these findings. Conclusions: ChatGPT-generated vignettes, when combined with human review, offer the potential for an efficient and scalable approach to capturing the experiences of patients with chronic pain. These vignettes could support training, communication, and sharing of patient insights.

Indexed as

BloggingChronic PainAdultFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedVideo RecordingGenAI or ChatGPTpatient narrativespatient-reported outcomesvideo blogvignettes

Identifiers

PMID42679095
PMCPMC13533317

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.